Abstract: This paper investigates the potential ofapproximate addition for k-means clustering, which is a popular unsupervised machine learning technique. The k-means clustering aims to organize data ...

Ballot (Balanced Lloyd with Optimal Transport) is a high-performance Python package for balanced clustering. It solves the problem of creating equal-sized clusters (or clusters with specific capacity ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

Automated apple harvesting is hindered by clustered fruits, varying illumination, and inconsistent depth perception in complex orchard environments. While deep learning models such as Faster R-CNN and ...

An ILTACON 2025 panel dove into how Microsoft Purview works, what it means for law firms and how it’s not a one-size-fits-all proposition. With Microsoft offerings fast becoming a staple across the ...

This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the ...

Abstract: K-means clustering is a popular technique for partitioning a data set into subsets of similar features. Due to their simple control flow and inherent fine-grain parallelism, K-means ...